Learning network topologies from data is very appealing. On the interpretable side, the structure of a network reveals important descriptors of the network itself, providing to humans a prompt and explainable decision support system; on the operative side, it is a requirement for processing and learning architectures operating on graph data, such as graph filters [1]}. When this structure is not readily available from the application, a fundamental question is how to learn it from data. The class of problems and the associated techniques concerning the identification of a network structure (from data) are known as graph topology identification (GTI), graph learning, or network topology inference [2]}, [3]}.
